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Workshop on Insights from Negative Results in NLP 2020 — accepted papers

Workshop on Insights from Negative Results in NLP 2020 is the 2020 edition of the Workshop on Insights from Negative Results in NLP, held in Online, November 2020 (publisher: Association for Computational Linguistics). The ACL Anthology lists 18 papers of it; our library holds 1 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

  1. Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary wordsAnmol Nayak, Hariprasad Timmapathini, Karthikeyan Ponnalagu et al.43 citations
  2. Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented DataWilliam Huang, Haokun Liu, Samuel R. Bowman32 citations · our summary
  3. HINT3: Raising the bar for Intent Detection in the WildGaurav Arora, Chirag Jain, Manas Chaturvedi et al.18 citations
  4. Label Propagation-Based Semi-Supervised Learning for Hate Speech ClassificationAshwin Geet D'Sa, Irina Illina, Dominique Fohr et al.17 citations
  5. Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic ModelsSilvia Terragni, Debora Nozza, Elisabetta Fersini et al.12 citations
  6. If You Build Your Own NER Scorer, Non-replicable Results Will ComeConstantine Lignos, Marjan Kamyab8 citations
  7. Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair TasksAnsel MacLaughlin, Jwala Dhamala, Anoop Kumar et al.7 citations
  8. The Extraordinary Failure of Complement Coercion CrowdsourcingYanai Elazar, Victoria Basmov, Shauli Ravfogel et al.4 citations

All accepted papers (18)

The most cited first; 1 with our summary link to their page here, the others to the original.

  1. Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary wordsAnmol Nayak, Hariprasad Timmapathini, Karthikeyan Ponnalagu et al.43 citations
  2. Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented DataWilliam Huang, Haokun Liu, Samuel R. Bowman32 citations · our summary
  3. HINT3: Raising the bar for Intent Detection in the WildGaurav Arora, Chirag Jain, Manas Chaturvedi et al.18 citations
  4. Label Propagation-Based Semi-Supervised Learning for Hate Speech ClassificationAshwin Geet D'Sa, Irina Illina, Dominique Fohr et al.17 citations
  5. Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic ModelsSilvia Terragni, Debora Nozza, Elisabetta Fersini et al.12 citations
  6. If You Build Your Own NER Scorer, Non-replicable Results Will ComeConstantine Lignos, Marjan Kamyab8 citations
  7. Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair TasksAnsel MacLaughlin, Jwala Dhamala, Anoop Kumar et al.7 citations
  8. The Extraordinary Failure of Complement Coercion CrowdsourcingYanai Elazar, Victoria Basmov, Shauli Ravfogel et al.4 citations
  9. Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?Valentina Beretta, Sébastien Harispe, Katarina Boland et al.3 citations
  10. Layout-Aware Text Representations Harm Clustering Documents by TypeCatherine Finegan-Dollak, Ashish Verma3 citations
  11. NMF Ensembles? Not for Text Summarization!Alka Khurana, Vasudha Bhatnagar3 citations
  12. Embedding Structured Dictionary EntriesSteven Wilson, Walid Magdy, Barbara McGillivray et al.1 citation
  13. On Task-Level Dialogue Composition of Generative Transformer ModelPrasanna Parthasarathi, Sharan Narang, Arvind Neelakantan

 Papers with our summary in our library (1)

The most cited first (OpenAlex citations plus our library's).

  1. Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented DataWilliam Huang, Haokun Liu, Samuel R. Bowman2020 · 50 citations

 Fields

Arts and Humanities (1)Computer Science (1)

 Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

 Top institutions

Institutions on the bylines of its works here, by the number of works.

Place and dates from the event's site or the ACL Anthology; works and citations from OpenAlex and our library.

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